Frontal Object Perception Using Radar and Mono-Vision

Abstract : In this paper, we detail a complete software architecture of a key task that an intelligent vehicle has to deal with: frontal object perception. This task is solved by processing raw data of a radar and a mono-camera to detect and track moving objects. Data sets obtained from highways, country roads and urban areas were used to test the proposed method. Several experiments were conducted to show that the proposed method obtains a better environment representation, i.e., reduces the false alarms and missed detections from individual sensor evidence.
Type de document :
Communication dans un congrès
2012 Intelligent Vehicles Symposium, Jun 2012, Alcalá de Henares, Spain. IEEE Conference Publications, pp.159-164, 2012, <10.1109/IVS.2012.6232307>
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00741151
Contributeur : Olivier Aycard <>
Soumis le : jeudi 11 octobre 2012 - 19:04:31
Dernière modification le : mardi 28 octobre 2014 - 18:34:58

Identifiants

Collections

Citation

Omar Chavez-Garcia, Julien Burlet, Trung-Dung Vu, Olivier Aycard. Frontal Object Perception Using Radar and Mono-Vision. 2012 Intelligent Vehicles Symposium, Jun 2012, Alcalá de Henares, Spain. IEEE Conference Publications, pp.159-164, 2012, <10.1109/IVS.2012.6232307>. <hal-00741151>

Partager

Métriques

Consultations de la notice

136